Best of Logging — 2025

1. 1
   Article

   Platformatic·1y

   Watt Admin: Your Local Node.js Monitoring Solution

   Watt Admin is an open-source monitoring and administration tool for Platformatic applications, providing real-time performance metrics, comprehensive logging, and full control over services. Key features include real-time memory and CPU usage monitoring, latency tracking, centralized log viewing and filtering, service management capabilities, and CLI integration. It is easy to set up with a simple command and is ideal for development diagnostics.

   203

   3
2. 2
   Article

   Halodoc·1y

   Log Standardization

   Debugging in microservices is challenging without structured logging. This post explores logging strategies for HTTP requests, Kafka events, and concurrency in Java, Golang, and Python. It addresses context loss in Java’s multi-threading by introducing MDCAwareCompletableFuture and MDCAwareExecutorService, ensuring traceability in concurrent systems. A structured logging approach with Request ID, Transaction ID, and Parent Transaction ID is recommended for better traceability and debugging efficiency.

   194

   1
3. 3
   Article

   Last9·1y

   Nginx Logging: A Complete Guide for Beginners

   Learn how to effectively set up, manage, and optimize Nginx logging for better debugging, monitoring, and performance insights. This guide covers log locations by OS, customizing log locations, log formats, techniques for better log analysis, log rotation management, solving common logging issues, and advanced logging configurations like JSON logging, conditional logging, and logging to Syslog.

   172

   1
4. 4
   Article

   Hacker News·22w

   Your Logs Are Lying To You

   Traditional logging practices fail in modern distributed systems because they produce fragmented, context-poor log lines that are difficult to search and correlate. The solution is "wide events" (also called canonical log lines): emitting one comprehensive, structured event per request per service that contains all relevant context—user data, business metrics, infrastructure details, and error information. This approach transforms debugging from text searching into structured querying, enabling complex questions to be answered with simple SQL-like queries. Key implementation strategies include building events throughout the request lifecycle, using tail-based sampling to keep all errors while sampling successful requests, and deliberately instrumenting code with business context rather than relying on auto-instrumentation alone.

   154

   3
5. 5
   Video

   Visual Studio Code·1y

   why you don't need console.log

   Reduce clutter from multiple console.log statements in your code by using log points in Visual Studio Code. Right-click on the gutter of the desired line, select 'Add Logpoint', and write your message. This method provides on-demand logging and eliminates the need for cleanup before deployment.

   142

   9
6. 6
   Article

   Last9·48w

   11 Best Log Monitoring Tools for Developers in 2025

   A comprehensive comparison of 11 log monitoring tools for developers in 2025, covering solutions from simple centralized logging (Papertrail) to enterprise-scale platforms (Datadog, Dynatrace). The guide evaluates each tool's strengths, limitations, pricing, and ideal use cases, while providing practical advice on choosing the right solution based on team size, log volume, and technical requirements. Key tools covered include Last9, Better Stack, Grafana Loki, Elastic Stack, and others, with emphasis on real-world implementation considerations like structured logging, query performance, and cost optimization.

   120

   3
7. 7
   Article

   Last9·1y

   How to Handle Logging in Microservices Architectures

   Effective logging in microservices requires standardized formats, centralized storage, correlation IDs, and strong security measures. The post provides strategies for building robust logging systems, including platform selection, structured data formats, and request tracing. It includes guidelines for optimizing log levels, setting up logging pipelines, addressing common logging mistakes, and ensuring log security. An example using Node.js illustrates practical implementation, and essential tools for effective microservices logging are highlighted.

   93

   1
8. 8
   Video

   Philipp Lackner·1y

   1h Spring Boot Logging Crash Course For Beginners (Config, Remote Logs, JSON Logging & More)

   This crash course introduces spring boot logging for beginners, emphasizing its importance in backend development. It covers advanced logging techniques such as configuring logs for different environments, remote logging using Grafana Loki, and implementing JSON formatted logs. The tutorial also includes setting up structured metadata and configuring a uniform log format for easier traceability and debugging.

   83
9. 9
   Article

   Awesome Java Newsletter·1y

   How To Do Logging In Java

   Discover the right logging library for your Java applications and understand how to configure and use them effectively. This guide covers legacy and modern logging libraries including java.util.logging, Log4j, Logback, and SLF4J. Learn best practices for logging, such as choosing appropriate log levels, ensuring proper message contents, and implementing centralized logging solutions within Java applications.

   79

   2
10. 10
    Article

    OpenTelemetry·1y

    OpenTelemetry Logging and You

    OpenTelemetry provides a comprehensive logging framework that includes logs, events, and spans. Logs are any telemetry data emitted through a log pipeline via the Logs API, while events are a specific type of log with a defined schema. Spans differ from events by having durations and hierarchical relationships. The design emphasizes correlating all telemetry signals through context for a cohesive observability solution.

    58
11. 11
    Article

    Salesforce Engineering·29w

    How Cursor AI Slashed Dashboard Migration Time 75%

    Salesforce's Mobile Performance Engineering team automated the migration of 20 production monitoring dashboards from Splunk to Tableau using Cursor AI and MCP Server, reducing engineering time by 75%. The migration involved translating 240 queries across dashboards while maintaining 99% production availability. Key technical challenges included converting Splunk's time series aggregation and automatic bucketing to SQL equivalents, mapping dynamic log fields to explicit table schemas, and handling complex date range translations. The team used iterative prompt engineering to teach AI tools to distinguish between syntax conversion and semantic intent, focusing on extracting raw data rather than preserving aggregations. Strategic validation through side-by-side query comparisons and confidence scoring ensured accuracy while preserving productivity gains. The freed engineering capacity enabled the team to shift from reactive migration work to proactive performance engineering, including shift-left AI adoption, preventive dashboards, and distributed tracing implementations.

    49

    1
12. 12
    Article

    ByteByteGo·30w

    How Nubank Built an In-house Logging Platform for 1 Trillion Log Entries

    Nubank built an in-house logging platform to replace a costly third-party vendor, handling 1 trillion daily log entries at 50% lower cost. The solution uses a two-phase architecture: an ingestion pipeline with Fluent Bit, custom buffering, and processing services, plus a query/storage layer combining Trino, AWS S3, and Parquet format. The platform processes 1 petabyte daily, maintains 45 petabytes of searchable data with 45-day retention, and serves 15,000 queries daily scanning 150 petabytes. Key design decisions included decoupling ingestion from querying, implementing micro-batching for reliability, and achieving 95% data compression with Parquet.

    49
13. 13
    Article

    Last9·1y

    The Complete Guide to Node.js Logging Libraries in 2025

    Effective logging is essential for debugging and monitoring Node.js applications. As applications grow in complexity, various logging libraries like Winston, Pino, and Bunyan provide solutions tailored for different use cases. This guide covers multiple libraries, highlighting their strengths and best use cases, and includes practical examples for implementing logging solutions. Performance, data structure, and integration capabilities are key considerations when choosing a library. Additionally, best practices such as using log levels, sanitizing sensitive data, and implementing correlation IDs in distributed systems are discussed.

    48

    1
14. 14
    Article

    Last9·1y

    How to Set Up Logging in Node.js (Without Overthinking It)

    Proper logging in Node.js is critical for debugging, performance insights, security, and operational intelligence. The post outlines the importance of logging, recommends tools like Winston, Pino, and Morgan, and provides a guide to setting up and utilizing these libraries effectively. It also discusses advanced logging strategies, error handling, and integrations with visualization and monitoring tools like Last9, Kibana, and Grafana.

    47

    2
15. 15
    Article

    Tigris·37w

    How LogSeam Searches 500 Million Logs per second

    LogSeam demonstrates how to build a high-performance security data lakehouse that searches 500 million logs per second with 1.5 TB/s throughput while reducing costs by 40-80%. The architecture converts JSON logs to compressed Parquet files, achieving 100x storage reduction, and uses globally distributed object storage with elastic compute for sub-second query responses across continents.

    46
16. 16
    Article

    Last9·1y

    Java Logging: Troubleshooting Tips and Best Practices

    Discover troubleshooting tips and best practices for Java logging. Learn about different Java logging frameworks like java.util.logging (JUL), Log4j 2, and SLF4J with Logback. Understand common logging issues, performance bottlenecks, and how to fix them. Get insights on choosing the right log levels, structuring log messages, and implementing centralized logging solutions.

    43
17. 17
    Article

    ClickHouse·30w

    How Netflix optimized its petabyte-scale logging system with ClickHouse

    Netflix processes 5 petabytes of logs daily using ClickHouse, handling 10.6 million events per second with sub-second query performance. Three key optimizations enabled this scale: replacing regex-based log fingerprinting with generated lexers (8-10x faster), implementing custom native protocol serialization for efficient data ingestion, and sharding tag maps to reduce query times from 3 seconds to 700ms. The system combines ClickHouse for hot data with Apache Iceberg for long-term storage, making logs searchable within 20 seconds while serving 500-1,000 queries per second across 40,000+ microservices.

    40

    1
18. 18
    Article

    The Data Engineer·43w

    We stopped relying on bloom filters and now sort our ClickHouse primary key on a resource fingerprint. It cut our log query scans to 0.85% of blocks

    A development team optimized ClickHouse log query performance by replacing bloom filter skip indexes with a deterministic resource fingerprint approach. They sort their primary key on a hash of cluster, namespace, and pod information, which groups logs from the same source together. This change reduced block scanning from nearly 100% to just 0.85% (222 out of 26,135 blocks) for single namespace queries, significantly improving I/O and latency. The team is now exploring ClickHouse's native JSON column type to further optimize GROUP BY operations.

    40
19. 19
    Article

    Last9·1y

    Log Levels: Answers to the Most Common Questions

    Logging is crucial for monitoring software systems, catching issues, and tracking behavior. Log levels categorize messages by importance, making it easier to filter and troubleshoot problems. This post explains common log levels like TRACE, DEBUG, INFO, WARN, ERROR, and FATAL, and how these compare to Syslog log levels. It also covers best practices for using log levels, how they impact performance, and their role in different logging frameworks and environments.

    40
20. 20
    Article

    Blueground Engineering·36w

    A Software Engineer’s Guide to Observability

    A comprehensive guide to observability for engineering teams, covering the three pillars (logging, tracing, metrics) and their practical applications. Explains why observability has become critical in the era of distributed systems and AI-generated code, where complexity is increasing while domain expertise is becoming more distributed. The guide focuses on understanding when and why to use different observability tools rather than just how to configure them.

    38
21. 21
    Article

    Spacelift·49w

    Docker Container Logs: How to View & Manage

    Docker container logs capture stdout and stderr output from containerized applications. The docker logs command provides various options to view, filter, and follow logs including timestamps, time ranges, and tail functionality. Docker stores logs locally by default using the json-file driver, but supports external logging drivers for production environments. Best practices include using centralized logging systems, structured logging formats, and proper log rotation to prevent disk space issues.

    34

    2
22. 22
    Article

    Last9·50w

    A Complete Guide to Linux Log File Locations and Their Usage

    Linux systems store logs primarily in /var/log/ directory using rsyslog and journald mechanisms. Key log files include /var/log/syslog for general system messages, /var/log/auth.log for authentication events, and application-specific logs for services like Apache, MySQL, and SSH. Essential command-line tools like tail, grep, less, and journalctl enable effective log analysis and real-time monitoring. The guide covers log rotation with logrotate, advanced filtering techniques using regular expressions, and systematic troubleshooting approaches for service failures, security incidents, and performance issues.

    32
23. 23
    Article

    Awesome Go·1y

    Recover panics in all Goroutines you start

    Panics in Go Goroutines can cause server crashes if not properly handled. Implementing a helper function, CatchPanic, for panic recovery ensures that errors are logged and the application continues running. Wrapping all manually created Goroutines with this helper can prevent entire application crashes and assist in diagnosing issues.

    31
24. 24
    Article

    Last9·1y

    .NET Logging with Serilog and OpenTelemetry

    Leverage Serilog and OpenTelemetry to enhance .NET application observability. Structured logs from Serilog make debugging easier, while OpenTelemetry unifies logs, metrics, and traces for deeper insights into system operations. This combination allows for better tracing of microservice requests, performance analysis, and context-rich debugging.

    28
25. 25
    Article

    Javarevisited·1y

    Aspect Oriented Programming (AOP) in Spring Boot

    Aspect Oriented Programming (AOP) helps manage cross-cutting concerns like logging, auditing, and security in Spring Boot applications. By centralizing these aspects, developers can maintain clean business logic and avoid redundant code. Key concepts include aspects, advice, join points, pointcuts, and weaving. AOP is beneficial for improving code readability, maintainability, and consistency. The choice between Spring AOP and AspectJ depends on specific performance needs and complexity of the application.

    28